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Demonstration
CodaLab Worksheets for Reproducible, Executable Papers
Percy Liang · Evelyne Viegas

Wed Dec 09 04:00 PM -- 08:55 PM (PST) @ 210D

We are interested in solving two infrastructural problems in data-centric fields such as machine learning: First, an inordinate amount of time is spent on preprocessing datasets, getting other people's code to run, writing evaluation/visualization scripts, with much of this effort duplicated across different research groups. Second, a only static set of final results are ever published, leaving it up to the reader to guess how the various methods would fare in unreported scenarios. We present CodaLab Worksheets, a new platform which aims to tackle these two problems by creating an online community around sharing and executing immutable components called bundles, thereby streamlining the research process.

Author Information

Percy Liang (Stanford University)
Percy Liang

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).

Evelyne Viegas (Microsoft Research)

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